Sample-Size Planning for Frequentist and Bayesian 2 × 2 Analysis-of-Variance Designs

A healthy lifestyle is fundamental for the prevention and treatment of cardiovascular disease and other noncommunicable diseases (NCDs). Investment in primary prevention, including modification of health risk behaviors, could result in a 4-fold improvement in health outcomes compared with secondary prevention based on pharmacological treatment. The American Heart Association (AHA) emphasized the importance of lifestyle in its 2020 goals for cardiovascular health promotion and disease reduction. In addition to defining “cardiovascular health” based on criteria for blood pressure and biochemical markers (lipids and glycemia), the AHA Strategic Planning Committee further identified lifestyle characteristics of central importance: nutrition, physical activity, smoking, and maintenance of a healthy body weight.1 The World Health Organization estimated that ≈80% of NCDs could be prevented if 4 key lifestyle practices were followed: a healthy diet, being physically active, avoidance of tobacco, and alcohol intake in moderation.2 To support healthy lifestyle initiatives, major changes are necessary at the societal level to improve population health. Numerous strategies might help to create a culture that promotes and facilitates healthy behaviors, including creating laws and regulations, mounting large-scale public awareness and education campaigns, implementing local community programs, and providing individual counseling.3 Physicians are uniquely positioned to encourage individuals to adopt healthy lifestyle behaviors: Approximately 80% of Americans visit their primary care physician at least once a year. Physicians directly communicate with their patients during clinical encounters across numerous settings, and research indicates that patients highly value recommendations provided by their physicians.4,5 However, data further indicate that lifestyle counseling does not routinely occur in physicians’ offices, thereby representing a lost opportunity. Physicians report that they perform lifestyle counseling during ≈34% of clinic visits.4 Patients, in turn, report an even lower frequency of physician lifestyle counseling. For example, obese patients reported receiving physical activity and …

Social Science and Its Frontiers Myron P. Gutmann (bio) Mark Solovey,Social Science for What? Battles over Public Funding for the “Other Sciences” at the National Science Foundation. Cambridge, Massachusetts: MIT Press, 2020. X+ 398pp. Figures, notes, index. $50.00. Americans often date the emergence of a strong commitment to government support of science to the launch of the Soviet Union’s Sputnik 1 satellite in October 1957. That event certainly spurred policy decisions that increased federal investments in education and science, and thus is an appropriate starting point for the popular narrative about science. At the same time, policy developments of the Sputnik era built on earlier events, widely recognized by historians of science. That perspective starts the story with the presentation in July 1946 of Vannever Bush’s report, Science, The Endless Frontier, to President Truman, advocating for a large, organized federal investment in scientific research, based on the role of science and technology in the Second World War. Early efforts to enact legislation based on the Bush report failed (Truman vetoed the first bill that passed because it lacked presidential control over the appointment of the Foundation’s leadership), but in 1950 Truman signed the National Science Foundation Act, establishing an enduring basis for publicly—especially federally—funded scientific research in the United States. The debates about the creation of the National Science Foundation pitted progressives against conservatives and advocates of public and congressional control of science against advocates of exclusive control by scientists.1 One of the topics of debate—although hardly the loudest—was whether the social sciences would be included in the Foundation’s charge.2 Vannever Bush was opposed to their inclusion, sometimes arguing that they should be supported by a separate organization; on the other side, Democratic West Virginia Senator Harley M. Kilgore, a leading sponsor of a more progressive approach, supported their inclusion in the Foundation’s mission. In the end, the compromise legislation that Truman signed in 1950 did not include support for the social sciences, but at the same time did not prohibit such support. The Foundation did not totally exclude the social sciences for long; it hired sociologist Harry Alpert in 1953, and in 1954 introduced a first, extremely modest, program to support the linkage between the social and natural sciences. [End Page 396] The first Social Sciences Division was not established until 1960 (in an era in which the Foundation was divided into four scientific divisions reflecting major disciplinary categories). Later, when the Foundation was reorganized into seven directorates (three of them disciplinary, one for education, and three for administrative activities) in 1975, the Divisions of Social Sciences and Behavioral and Neural Sciences were part of an expanded Directorate for Biological, Behavioral and Social Sciences (p. 179). Only in 1991–92 did the Foundation establish a separate Directorate for the Social, Behavioral and Economic (SBE) Sciences, an organizational status that still exists today. The road from the origin of the Foundation to the creation of the SBE Directorate was not linear, with ups and downs in support for the social and behavioral sciences mostly reflecting political and institutional challenges. This history spanning the period from the first discussions of the National Science Foundation through the end of the 1980s (with an added discussion of recent events and recommendations for the future) is the topic of Mark Solovey’s Social Science for What? Battles over Public Funding for the “Other Sciences” at the National Science Foundation. In this book he builds on his earlier book, Shaky Foundations: The Politics-Patronage-Social Science Nexus in Cold War America (2013), on extensive archival research, and on interviews with surviving participants. Social Science for What? is an impressive accomplishment, capturing the connections between partisan politics, scientific inquiry, tensions among scientific disciplines, and the institutional development of the Foundation. It is instructive for all readers, including for me, who served for four years (2009–13) as one of the Foundation’s Assistant Directors and head of the Directorate for Social Behavioral and Economic Sciences (SBE). Social Science for What? articulates consistent themes that define social science at NSF, along with a lively narrative arc. To define that arc, Solovey divides the main...

Populations are frequently nonnormally distributed in applied behavioral and social science research, affecting the properties of the F test in Analysis of Variance designs. The purpose of this study is to compare some of the most promising nonparametric alternatives to the F test for interaction:McSweeney (Marascuilo and McSweeney, 1977), Harwell and Serlin (1989; Harwell, 1991), and Blair and Sawilowsky (1990). The properties of the competing statistics were examined on data sampled from four theoretical distributions and two real data sets in the context of the balanced 2 × 2 × 2 design. Cell sample sizes of n= 7, 21, and 35 were obtained, and statistics were calculated at the nominal α = 0.05 and 0.01 levels. The number of repetitions was set at 20,000 per experiment. Results indicated that Harwell and Serlin's L test was conservatively and liberally nonrobust, and not a powerful competitor. Although robust, McSweeney's test was conservative when the effect size was large, and as the number of nonnull effects increased, the test's power decreased. If the distribution is heavy-tailed or skewed, the Blair and Sawilowsky test statistic demonstrated superior power properties when compared with ANOVA. The F test retained a slight power advantage over the Blair and Sawilowsky statistic when testing for interactions on data sampled from populations that are symmetric with light tails, such as the normal or uniform.

The majority of respondents who took part in a survey were of the opinion that there should be a greater focus placed on behavioural and social sciences within the curriculum of medical schools. This is done to ensure that graduates of medical schools will be able to practise medicine in a manner that is both safe and effective. Despite the fact that behavioural and social sciences make significant contributions to the effectiveness of health care delivery, traditional medical school curricula have not traditionally placed a significant amount of focus on the study of these subjects. This article's objective is to provide the reader with a more in-depth comprehension of the value of social and behavioural sciences in medical education as well as the breadth of their application in a variety of different settings. Additionally, it discusses the areas of social and behavioural sciences that are significant to medicine, as well as the efficacy of incorporating them into the curricula of medical schools in order to educate and train future medical professionals to practise medicine in a manner that is fully informed. Place of Study: Foundation University Islamabad Study Duration: February 2022 to July 2022 Study Design: Empirical research Conclusion: This study examines the importance of teaching future doctors about medicine's social and behavioural aspects. It gives medical school educators the latest information on how to best teach medical students to succeed in the medical industry. Medical educators, administrators, policymakers, and other stakeholders must work together to integrate social and behavioural sciences into medical curricula. Keywords: Medical curriculum's courses, the social and behavioral sciences, and the foundations of medical education.

Proposed Revisions to the Common Rule for the Protection of Human Subjects in the Behavioral and Social Sciences examines how to update human subjects protections regulations so that they effectively respond to current research contexts and methods. With a specific focus on social and behavioral sciences, this consensus report aims to address the dramatic alterations in the research landscapes that institutional review boards (IRBs) have come to inhabit during the past 40 years. The report aims to balance respect for the individual persons whose consent to participate makes research possible and respect for the social benefits that productive research communities make possible.The ethics of human subjects research has captured scientific and regulatory attention for half a century. To keep abreast of the universe of changes that factor into the ethical conduct of research today, the Department of Health and Human Services published an Advance Notice of Proposed Rulemaking (ANPRM) in July 2011. Recognizing that widespread technological and societal transformations have occurred in the contexts for and conduct of human research since the passage of the National Research Act of 1974, the ANPRM revisits the regulations mandated by the Act in a correspondingly comprehensive manner. Its proposals aim to modernize the Common Rule and to improve the efficiency of the work conducted under its auspices. Proposed Revisions to the Common Rule for the Protection of Human Subjects in the Behavioral and Social Sciences identifies issues raised in the ANPRM that are critical and feasible for the federal government to address for the protection of participants and for the advancement of the social and behavioral sciences. For each identified issue, this report provides guidance for IRBs on techniques to address it, with specific examples and best practice models to illustrate how the techniques would be applied to different behavioral and social sciences research procedures.

Political science and other closely related social science disciplines could certainly benefit from the creation of a Directorate for the Social and Behavioral Sciences within the National Science Foundation. I present the case for such organizational restructuring on behalf of the American Political Science Association and the Western Political Science Association, and as a charter member and former President of the Social Science History Association. That a benefit would accrue from a reorganization would seem likely in the face of two organizational imperatives. First, political science and its sister disciplines need direct representation by senior officers of their own directorate in the policy making and resource allocation of at least three additional existing directorates: the Directorate for Computer and Information Science and Engineering, the Directorate for Education and Human Resources, and the Directorate for Scientific, Technological and International Affairs. The needs of political science in these three domains are similar to those of the other social sciences and are distinctly different from the needs of either the life sciences, the geosciences or the mathematical and physical sciences. Social science needs will not, and most likely cannot, be articulated by Foundation officers whose organizational responsibilities are overwhelmingly defined by the needs -and current resources-of the biosciences and whose professional backgrounds lie in one of the biosciences. We believe that the manifold resources of the Foundationprofessional and technical as well as budgetary-have not successfully addressed the needs of the social sciences in large part because the social sciences are not directly represented at the appropriate organizational level within the Foundation. The second organizational imperative stems from the need for greater organizational differentiation within the social sciences. Even though few of the social and behavioral science disciplines are as diverse as the array of subfields in chemistry or its sister disciplines, the full panoply of research specialties across the several social sciences is on a par with the diversity represented in the other substantive directorates. Many of the existing activities of the present Division of Social and Economic Science could be relocated as divisions of the new directorate. For example, without attempting to provide an organizational blueprint for the future, it may be suggested that, as with the other substantive directorates, each of the present disciplinary programs in S.E.S. might well be a division within a Social and Behavioral Science Directorate. They might be joined by a Division of Methods, Measurement and Instrumentation needed to address those problems of data generation and analysis that the disciplinary divisions have in common. Similarly, there should also be a separate division for large-scale multi-purpose data collection and resource development. A quite new division might also be established for activities centered on increasing the scientific usefulness of data generated by governmental agencies. Finally, and still illustratively, a separate division might be created for multi-disciplinary or multi-institutional projects or programs. To give greater clarity to the foregoing prescriptions, consider the following. First, with regard to representing social science needs in other directorates, the computer has become as central-and totally indispensable-to the work ways of social science as to the other sciences. And yet the central tasks for the computer are somewhat different. Certainly in contrast to mathematics, social science does much more data management of numeric data, more archiving and retrieval of non-quantitative materials, and much less sheer computation. On a quite different dimension, social science has its own version of the adaptation of the computer to data generation. In the harnessing of the computer in Computer Assisted Telephone Interviewing, in improving methods of textual analysis, and in the use of the lap top computer for data collection in the field (and quite apart from use in simulation exercises), we are only beginning to exploit fully this technological wonder. As a third illustration, it can be noted that in the absence of large laboratories or research centers which bring together scientists working on common problems, the computer network is becoming an essential feature of the social scientist's life. Both the transmission of data by computer nets and inter-personal exchanges among scientists are probably more crucial for the maturing social sciences than for the more developed disciplines. Many of these and other needs of the social scientist are served indirectly and inadvertently by computer developments in other realms. However, without a new directorate in the Foundation, it seems unrealistic if not unreasonable to expect strong and direct representation of social science computing needs that should affect future Foundation policy and resource allocation. A separate Directorate for the Social and Behavioral Sciences is a necessary, if not sufficient, condition to have an impact on Foundation decisions concerning the development of computer and information science.

Hume observed that our minds are mirrors to one another: they reflect one another's passions, sentiments, and opinions.' This "sympathy," or "propensity we have to sympathize with others, to ... receive by communication [the] inclinations and sentiments [of others], however different from, or even contrary to, our own," he held to be the chief source of moral distinctions.2 Hume presented an account of how this mirroring of minds works. After a brief presentation of the account, I will show how it needs to be updated and corrected in the light of recent empirical research. Then I will give some reasons to think that the mirroring of minds is more pervasive than even Hume had thought: that mirroring is an essential part of the way in which we think about other minds. Finally, I will make some remarks about the relevance of mirroring to ethics.

Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness. The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families. The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services. Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.

Objectives Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness. Methods The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families. Results The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services. Conclusions Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.KeywordsArtificial intelligenceBehavioral and social sciencesMachine learningFamiliesChildren

Etude des deux formes de communication utilisee par les humains : le langage et la communication non verbale ; cette derniere forme etant utilisee par les primates pour communiquer. Commentaires, reponse de l'auteur.

The National Institute for Health and Care Research accepts applications for pilot and feasibility studies to their Research for Patient Benefit (RfPB) programme. There has been limited work describing the design practices of these applications and funding status. Knowing some of the qualities which may contribute towards a pilot or feasibility study application successfully gaining funding could help researchers improve the quality of their applications. Therefore, this study describes the protocol for a review looking at the characteristics of funded and non-funded external pilot trial applications. In particular, the primary objective is to describe the planned sample size and sample size justifications. The study will be conducted on 100 applications from Competition 31-37 with a randomised feasibility design, identified and given access to us by RfPB where the lead applicant has consented. We will screen these applications to identify the external pilot trials, first looking through the titles and then the full text. Following this, we will extract data on information such as medical area, study design, objective(s), sample size, sample size justification, and funding outcome stage one and two. Validation will be performed on 20% of the data extracted; discrepancies will be resolved by discussion or a third reviewer will decide if there is no consensus. We will use descriptive statistics to summarise quantitative data, and will analyse qualitative data using thematic analysis. Findings will be summarised through discussion with the project contributors to produce a reader-friendly guidance document. This work will provide a more complete picture of RfPB external randomised pilot and feasibility trials. The findings will assist researchers when planning their pilot trials, and could help improve the quality of submitted applications. Open Science Framework protocol registration DOI: https://doi.org/10.17605/OSF.IO/PYKVG.

Social and behavioral sciences, a cross-disciplinary field that examines the interaction among behavioral, biological, environmental, and social factors, has contributed immensely to some public health achievements over the last century. Through collaboration with community organizations and partners, social and behavioral scientists have conducted numerous program interventions involving community engagement and advocacy efforts at the local, state, federal, and international levels. This article traces select historical underpinnings of the applications of social and behavioral sciences theories and evidence to public health and highlights 4 areas in which health education specialists have distinctly contributed to public health achievements by building on theory and evidence. Applied social and behavioral sciences have formed the basis of various health education interventions. These 4 areas include the following: (1) Theory, Model Development, and the Professionalization of Health Education; (2) Participation and Community Engagement; (3) Health Communication; and (4) Advocacy and Policy. We present contemporary challenges and recommendations for strengthening the theory, research, and practice of health education within the context of social and behavioral sciences in addressing emerging public health issues.

Increasing blood donor recruitment and retention is of key importance to transfusion services. Research within the social and behavioral science traditions has adopted separate but complementary approaches to addressing these issues. This article aims to review both of these types of literature, examine theoretical developments, identify commonalities, and offer a means to integrate these within a single intervention approach. The social and behavioral science literature on blood donor recruitment and retention focusing on theory, interventions, and integration is reviewed. The role of emotional regulation (anticipated anxiety and vasovagal reactions) is central to both the behavioral and the social science approaches to enhancing donor motivation, yet although intentions are the best predictor of donor behavior, interventions targeting enactment of intentions have not been used to increase donation. Implementation intentions (that is, if-then plans formed in advance of acting) provide a useful technique to integrate findings from social and behavioral sciences to increase donor recruitment and retention. After reviewing the literature, implementation intention formation is proposed as a technique to integrate the key findings and theories from the behavioral and social science literature on blood donor recruitment and retention.

ABSTRACTThe interplay between the Bayesian and Frequentist approaches: a general nesting spatial panel-data model. Spatial Economic Analysis. An econometric framework mixing the Frequentist and Bayesian approaches is proposed in order to estimate a general nesting spatial model. First, it avoids specific dependency structures between unobserved heterogeneity and regressors, which improves mixing properties of Markov chain Monte Carlo (MCMC) procedures in the presence of unobserved heterogeneity. Second, it allows model selection based on a strong statistical framework, characteristics that are not easily introduced using a Frequentist approach. We perform some simulation exercises, finding good performance of the properties of our approach, and apply the methodology to analyse the relation between productivity and public investment in the United States.

Communication Climate as Predictor of Perceived Corporate Governance and Organizational Success